Overview

Dataset statistics

Number of variables13
Number of observations2344823
Missing cells0
Missing cells (%)0.0%
Duplicate rows3438
Duplicate rows (%)0.1%
Total size in memory125.2 MiB
Average record size in memory56.0 B

Variable types

Numeric8
Categorical5

Alerts

Dataset has 3438 (0.1%) duplicate rowsDuplicates
IN_TREINEIRO is highly overall correlated with TP_FAIXA_ETARIA and 1 other fieldsHigh correlation
Q001 is highly overall correlated with Q002 and 1 other fieldsHigh correlation
Q002 is highly overall correlated with Q001 and 1 other fieldsHigh correlation
Q003 is highly overall correlated with Q001High correlation
Q004 is highly overall correlated with Q002High correlation
TP_ESCOLA is highly overall correlated with TP_ST_CONCLUSAOHigh correlation
TP_FAIXA_ETARIA is highly overall correlated with IN_TREINEIROHigh correlation
TP_ST_CONCLUSAO is highly overall correlated with IN_TREINEIRO and 1 other fieldsHigh correlation
TP_COR_RACA has 40871 (1.7%) zerosZeros

Reproduction

Analysis started2024-04-15 02:47:13.733355
Analysis finished2024-04-15 02:48:20.884816
Duration1 minute and 7.15 seconds
Software versionydata-profiling vv4.7.0
Download configurationconfig.json

Variables

TP_FAIXA_ETARIA
Real number (ℝ)

HIGH CORRELATION 

Distinct20
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.2380606
Minimum1
Maximum20
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.9 MiB
2024-04-14T23:48:20.931859image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q35
95-th percentile12
Maximum20
Range19
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.3427183
Coefficient of variation (CV)0.78873774
Kurtosis2.2625056
Mean4.2380606
Median Absolute Deviation (MAD)1
Skewness1.6837301
Sum9937502
Variance11.173766
MonotonicityNot monotonic
2024-04-14T23:48:21.022943image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
3 593355
25.3%
2 576153
24.6%
4 269293
11.5%
1 247749
10.6%
5 151508
 
6.5%
6 95792
 
4.1%
11 88159
 
3.8%
7 67515
 
2.9%
8 49832
 
2.1%
12 47083
 
2.0%
Other values (10) 158384
 
6.8%
ValueCountFrequency (%)
1 247749
10.6%
2 576153
24.6%
3 593355
25.3%
4 269293
11.5%
5 151508
 
6.5%
6 95792
 
4.1%
7 67515
 
2.9%
8 49832
 
2.1%
9 36867
 
1.6%
10 30176
 
1.3%
ValueCountFrequency (%)
20 315
 
< 0.1%
19 852
 
< 0.1%
18 2090
 
0.1%
17 5149
 
0.2%
16 9309
 
0.4%
15 15339
 
0.7%
14 23948
 
1.0%
13 34339
 
1.5%
12 47083
2.0%
11 88159
3.8%

TP_COR_RACA
Real number (ℝ)

ZEROS 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.991332
Minimum0
Maximum5
Zeros40871
Zeros (%)1.7%
Negative0
Negative (%)0.0%
Memory size8.9 MiB
2024-04-14T23:48:21.109020image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q33
95-th percentile3
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.0184765
Coefficient of variation (CV)0.51145492
Kurtosis-1.3097986
Mean1.991332
Median Absolute Deviation (MAD)1
Skewness0.1328009
Sum4669321
Variance1.0372944
MonotonicityNot monotonic
2024-04-14T23:48:21.191096image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 1026418
43.8%
3 966698
41.2%
2 255863
 
10.9%
4 43782
 
1.9%
0 40871
 
1.7%
5 11191
 
0.5%
ValueCountFrequency (%)
0 40871
 
1.7%
1 1026418
43.8%
2 255863
 
10.9%
3 966698
41.2%
4 43782
 
1.9%
5 11191
 
0.5%
ValueCountFrequency (%)
5 11191
 
0.5%
4 43782
 
1.9%
3 966698
41.2%
2 255863
 
10.9%
1 1026418
43.8%
0 40871
 
1.7%

TP_ST_CONCLUSAO
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size17.9 MiB
1
963119 
2
957731 
3
417070 
4
 
6903

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2344823
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row2

Common Values

ValueCountFrequency (%)
1 963119
41.1%
2 957731
40.8%
3 417070
17.8%
4 6903
 
0.3%

Length

2024-04-14T23:48:21.275171image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-14T23:48:21.355244image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
1 963119
41.1%
2 957731
40.8%
3 417070
17.8%
4 6903
 
0.3%

Most occurring characters

ValueCountFrequency (%)
1 963119
41.1%
2 957731
40.8%
3 417070
17.8%
4 6903
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2344823
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 963119
41.1%
2 957731
40.8%
3 417070
17.8%
4 6903
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2344823
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 963119
41.1%
2 957731
40.8%
3 417070
17.8%
4 6903
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2344823
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 963119
41.1%
2 957731
40.8%
3 417070
17.8%
4 6903
 
0.3%

TP_ESCOLA
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size17.9 MiB
1
1387092 
2
760853 
3
196878 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2344823
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row3

Common Values

ValueCountFrequency (%)
1 1387092
59.2%
2 760853
32.4%
3 196878
 
8.4%

Length

2024-04-14T23:48:21.439321image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-14T23:48:21.510385image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
1 1387092
59.2%
2 760853
32.4%
3 196878
 
8.4%

Most occurring characters

ValueCountFrequency (%)
1 1387092
59.2%
2 760853
32.4%
3 196878
 
8.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2344823
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 1387092
59.2%
2 760853
32.4%
3 196878
 
8.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2344823
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 1387092
59.2%
2 760853
32.4%
3 196878
 
8.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2344823
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 1387092
59.2%
2 760853
32.4%
3 196878
 
8.4%

IN_TREINEIRO
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size17.9 MiB
0
1927753 
1
417070 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2344823
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1927753
82.2%
1 417070
 
17.8%

Length

2024-04-14T23:48:21.594461image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-14T23:48:21.673533image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1927753
82.2%
1 417070
 
17.8%

Most occurring characters

ValueCountFrequency (%)
0 1927753
82.2%
1 417070
 
17.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2344823
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1927753
82.2%
1 417070
 
17.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2344823
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1927753
82.2%
1 417070
 
17.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2344823
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1927753
82.2%
1 417070
 
17.8%

TP_LINGUA
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size17.9 MiB
0
1357622 
1
987201 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2344823
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 1357622
57.9%
1 987201
42.1%

Length

2024-04-14T23:48:21.759611image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-14T23:48:21.827674image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1357622
57.9%
1 987201
42.1%

Most occurring characters

ValueCountFrequency (%)
0 1357622
57.9%
1 987201
42.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2344823
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1357622
57.9%
1 987201
42.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2344823
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1357622
57.9%
1 987201
42.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2344823
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1357622
57.9%
1 987201
42.1%

Q001
Real number (ℝ)

HIGH CORRELATION 

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.5935045
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.9 MiB
2024-04-14T23:48:21.893237image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q13
median5
Q36
95-th percentile8
Maximum8
Range7
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.8760278
Coefficient of variation (CV)0.40840883
Kurtosis-0.75607041
Mean4.5935045
Median Absolute Deviation (MAD)1
Skewness0.03745095
Sum10770955
Variance3.5194803
MonotonicityNot monotonic
2024-04-14T23:48:21.979316image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
5 720913
30.7%
2 350284
14.9%
3 291859
12.4%
4 259673
 
11.1%
6 250540
 
10.7%
8 202637
 
8.6%
7 193050
 
8.2%
1 75867
 
3.2%
ValueCountFrequency (%)
1 75867
 
3.2%
2 350284
14.9%
3 291859
12.4%
4 259673
 
11.1%
5 720913
30.7%
6 250540
 
10.7%
7 193050
 
8.2%
8 202637
 
8.6%
ValueCountFrequency (%)
8 202637
 
8.6%
7 193050
 
8.2%
6 250540
 
10.7%
5 720913
30.7%
4 259673
 
11.1%
3 291859
12.4%
2 350284
14.9%
1 75867
 
3.2%

Q002
Real number (ℝ)

HIGH CORRELATION 

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.8018234
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.9 MiB
2024-04-14T23:48:22.058244image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q14
median5
Q36
95-th percentile7
Maximum8
Range7
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.6215216
Coefficient of variation (CV)0.33768873
Kurtosis-0.43221959
Mean4.8018234
Median Absolute Deviation (MAD)1
Skewness-0.32283434
Sum11259426
Variance2.6293324
MonotonicityNot monotonic
2024-04-14T23:48:22.140376image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
5 851162
36.3%
6 329384
 
14.0%
7 322603
 
13.8%
4 262334
 
11.2%
2 238683
 
10.2%
3 232165
 
9.9%
8 62486
 
2.7%
1 46006
 
2.0%
ValueCountFrequency (%)
1 46006
 
2.0%
2 238683
 
10.2%
3 232165
 
9.9%
4 262334
 
11.2%
5 851162
36.3%
6 329384
 
14.0%
7 322603
 
13.8%
8 62486
 
2.7%
ValueCountFrequency (%)
8 62486
 
2.7%
7 322603
 
13.8%
6 329384
 
14.0%
5 851162
36.3%
4 262334
 
11.2%
3 232165
 
9.9%
2 238683
 
10.2%
1 46006
 
2.0%

Q003
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2131082
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.9 MiB
2024-04-14T23:48:22.223448image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile6
Maximum6
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.5404451
Coefficient of variation (CV)0.47942521
Kurtosis-0.86074084
Mean3.2131082
Median Absolute Deviation (MAD)1
Skewness0.24137501
Sum7534170
Variance2.372971
MonotonicityNot monotonic
2024-04-14T23:48:22.302520image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3 531108
22.7%
4 525019
22.4%
2 437711
18.7%
1 387595
16.5%
6 260803
11.1%
5 202587
 
8.6%
ValueCountFrequency (%)
1 387595
16.5%
2 437711
18.7%
3 531108
22.7%
4 525019
22.4%
5 202587
 
8.6%
6 260803
11.1%
ValueCountFrequency (%)
6 260803
11.1%
5 202587
 
8.6%
4 525019
22.4%
3 531108
22.7%
2 437711
18.7%
1 387595
16.5%

Q004
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0045539
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.9 MiB
2024-04-14T23:48:22.378589image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q34
95-th percentile6
Maximum6
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.4813366
Coefficient of variation (CV)0.49303048
Kurtosis-0.81308274
Mean3.0045539
Median Absolute Deviation (MAD)1
Skewness0.48345606
Sum7045147
Variance2.1943582
MonotonicityNot monotonic
2024-04-14T23:48:22.467398image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2 902602
38.5%
4 645302
27.5%
1 309181
 
13.2%
6 196040
 
8.4%
5 149110
 
6.4%
3 142588
 
6.1%
ValueCountFrequency (%)
1 309181
 
13.2%
2 902602
38.5%
3 142588
 
6.1%
4 645302
27.5%
5 149110
 
6.4%
6 196040
 
8.4%
ValueCountFrequency (%)
6 196040
 
8.4%
5 149110
 
6.4%
4 645302
27.5%
3 142588
 
6.1%
2 902602
38.5%
1 309181
 
13.2%

Q006
Real number (ℝ)

Distinct17
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.0370689
Minimum1
Maximum17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.9 MiB
2024-04-14T23:48:22.547360image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q37
95-th percentile14
Maximum17
Range16
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.7950422
Coefficient of variation (CV)0.75342273
Kurtosis1.4457602
Mean5.0370689
Median Absolute Deviation (MAD)2
Skewness1.4291988
Sum11811035
Variance14.402345
MonotonicityNot monotonic
2024-04-14T23:48:22.631249image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
2 630492
26.9%
3 369704
15.8%
4 276804
11.8%
5 194527
 
8.3%
8 145866
 
6.2%
7 145812
 
6.2%
1 119268
 
5.1%
6 115203
 
4.9%
9 62344
 
2.7%
10 44115
 
1.9%
Other values (7) 240688
 
10.3%
ValueCountFrequency (%)
1 119268
 
5.1%
2 630492
26.9%
3 369704
15.8%
4 276804
11.8%
5 194527
 
8.3%
6 115203
 
4.9%
7 145812
 
6.2%
8 145866
 
6.2%
9 62344
 
2.7%
10 44115
 
1.9%
ValueCountFrequency (%)
17 39060
 
1.7%
16 29282
 
1.2%
15 31826
 
1.4%
14 28365
 
1.2%
13 39300
 
1.7%
12 41407
 
1.8%
11 31448
 
1.3%
10 44115
 
1.9%
9 62344
2.7%
8 145866
6.2%

Q024
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size17.9 MiB
1
988546 
2
932055 
3
424222 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2344823
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 988546
42.2%
2 932055
39.7%
3 424222
18.1%

Length

2024-04-14T23:48:22.724159image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-14T23:48:22.794225image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
1 988546
42.2%
2 932055
39.7%
3 424222
18.1%

Most occurring characters

ValueCountFrequency (%)
1 988546
42.2%
2 932055
39.7%
3 424222
18.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2344823
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 988546
42.2%
2 932055
39.7%
3 424222
18.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2344823
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 988546
42.2%
2 932055
39.7%
3 424222
18.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2344823
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 988546
42.2%
2 932055
39.7%
3 424222
18.1%

NU_NOTA_MEDIA
Real number (ℝ)

Distinct50309
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean543.48471
Minimum0
Maximum855.98
Zeros22
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size17.9 MiB
2024-04-14T23:48:22.883306image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile402.08
Q1484.52
median540.54
Q3602.06
95-th percentile693.14
Maximum855.98
Range855.98
Interquartile range (IQR)117.54

Descriptive statistics

Standard deviation88.04023
Coefficient of variation (CV)0.1619921
Kurtosis-0.016444533
Mean543.48471
Median Absolute Deviation (MAD)58.58
Skewness0.034281316
Sum1.2743754 × 109
Variance7751.0822
MonotonicityNot monotonic
2024-04-14T23:48:22.990170image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
526.72 261
 
< 0.1%
557.2 259
 
< 0.1%
534.74 255
 
< 0.1%
531.5 254
 
< 0.1%
530.78 253
 
< 0.1%
513.4 251
 
< 0.1%
514.72 249
 
< 0.1%
529.5 249
 
< 0.1%
512.76 247
 
< 0.1%
530.6 246
 
< 0.1%
Other values (50299) 2342299
99.9%
ValueCountFrequency (%)
0 22
< 0.1%
56.14 1
 
< 0.1%
64 1
 
< 0.1%
66.1 1
 
< 0.1%
69.8 1
 
< 0.1%
72.12 1
 
< 0.1%
80 1
 
< 0.1%
82.28 1
 
< 0.1%
89.12 1
 
< 0.1%
92 1
 
< 0.1%
ValueCountFrequency (%)
855.98 1
< 0.1%
855.82 1
< 0.1%
851.84 1
< 0.1%
849.86 1
< 0.1%
848.32 1
< 0.1%
843.5 1
< 0.1%
842.02 1
< 0.1%
841.98 1
< 0.1%
841.76 1
< 0.1%
841.1 1
< 0.1%

Interactions

2024-04-14T23:48:13.413562image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-14T23:47:49.942023image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-14T23:47:53.218596image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-14T23:47:56.454838image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-14T23:47:59.811889image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-14T23:48:03.220301image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-14T23:48:06.491273image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-14T23:48:10.046504image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-14T23:48:13.816929image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-14T23:47:50.383342image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-14T23:47:53.622718image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-14T23:47:56.854201image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-14T23:48:00.258294image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-14T23:48:03.629673image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-14T23:48:06.919662image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-14T23:48:10.493909image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-14T23:48:14.278348image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-14T23:47:50.784287image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-14T23:47:53.995056image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-14T23:47:57.249560image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-14T23:48:00.640199image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-14T23:48:04.039045image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-14T23:48:07.388088image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-14T23:48:10.889269image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-14T23:48:14.668702image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-14T23:47:51.195443image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-14T23:47:54.419442image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-14T23:47:57.644920image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-14T23:48:01.027512image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-14T23:48:04.466433image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-14T23:48:07.804466image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-14T23:48:11.344682image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-14T23:48:15.093088image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-14T23:47:51.601376image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-14T23:47:54.807905image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-14T23:47:58.040279image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-14T23:48:01.466911image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-14T23:48:04.832766image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-14T23:48:08.261882image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-14T23:48:11.739041image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-14T23:48:15.516472image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-14T23:47:51.984055image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-14T23:47:55.211272image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-14T23:47:58.496694image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-14T23:48:01.848316image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-14T23:48:05.269163image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-14T23:48:08.696276image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-14T23:48:12.167430image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-14T23:48:15.926845image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-14T23:47:52.407033image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-14T23:47:55.623004image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-14T23:47:58.926084image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-14T23:48:02.298342image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-14T23:48:05.640500image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-14T23:48:09.186721image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-14T23:48:12.554782image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-14T23:48:16.391107image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-14T23:47:52.818069image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-14T23:47:56.030375image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-14T23:47:59.408521image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-14T23:48:02.742868image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-14T23:48:06.066887image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-14T23:48:09.615111image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-14T23:48:12.972161image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Correlations

2024-04-14T23:48:23.068237image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
IN_TREINEIRONU_NOTA_MEDIAQ001Q002Q003Q004Q006Q024TP_COR_RACATP_ESCOLATP_FAIXA_ETARIATP_LINGUATP_ST_CONCLUSAO
IN_TREINEIRO1.0000.0120.1160.1600.0870.1130.1590.125-0.0680.386-0.5820.1091.000
NU_NOTA_MEDIA0.0121.0000.2560.3150.2480.2780.4630.306-0.2280.192-0.0730.2770.064
Q0010.1160.2561.0000.5010.5020.3570.3750.350-0.1600.204-0.2180.2600.124
Q0020.1600.3150.5011.0000.3490.5190.4660.325-0.1770.191-0.2830.2360.134
Q0030.0870.2480.5020.3491.0000.4970.3870.364-0.1650.219-0.1520.2670.103
Q0040.1130.2780.3570.5190.4971.0000.4640.347-0.1830.201-0.1940.2490.107
Q0060.1590.4630.3750.4660.3870.4641.0000.485-0.2900.252-0.2170.2990.111
Q0240.1250.3060.3500.3250.3640.3470.4851.000-0.2580.200-0.1330.2770.094
TP_COR_RACA-0.068-0.228-0.160-0.177-0.165-0.183-0.290-0.2581.0000.1110.1100.1800.066
TP_ESCOLA0.3860.1920.2040.1910.2190.2010.2520.2000.1111.000-0.3210.1340.707
TP_FAIXA_ETARIA-0.582-0.073-0.218-0.283-0.152-0.194-0.217-0.1330.110-0.3211.0000.1770.498
TP_LINGUA0.1090.2770.2600.2360.2670.2490.2990.2770.1800.1340.1771.0000.136
TP_ST_CONCLUSAO1.0000.0640.1240.1340.1030.1070.1110.0940.0660.7070.4980.1361.000

Missing values

2024-04-14T23:48:16.532235image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-14T23:48:17.855437image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

TP_FAIXA_ETARIATP_COR_RACATP_ST_CONCLUSAOTP_ESCOLAIN_TREINEIROTP_LINGUAQ001Q002Q003Q004Q006Q024NU_NOTA_MEDIA
0521101561421558.24
1631101311212394.62
2621101552221414.10
3431101552221438.10
4212301552121576.70
5233110766622530.58
6821101261421645.80
7133110853222378.74
8411101244222500.40
9431101552251605.58
TP_FAIXA_ETARIATP_COR_RACATP_ST_CONCLUSAOTP_ESCOLAIN_TREINEIROTP_LINGUAQ001Q002Q003Q004Q006Q024NU_NOTA_MEDIA
23448131231100346273599.36
2344814212200453362526.38
2344815601100251141533.66
2344816321100556222467.20
2344817312200563462515.02
23448181211101553331488.40
23448191121101836342617.92
2344820232200853221541.22
23448211111100322221507.22
2344822212200533272607.06

Duplicate rows

Most frequently occurring

TP_FAIXA_ETARIATP_COR_RACATP_ST_CONCLUSAOTP_ESCOLAIN_TREINEIROTP_LINGUAQ001Q002Q003Q004Q006Q024NU_NOTA_MEDIA# duplicates
2360332201221121447.904
78113110664473620.103
1081131106655173643.983
2811131107755163635.923
3951131107755173633.723
4001131107755173636.743
4121131107755173645.703
4281131107755173653.163
4311131107755173655.163
4411131107755173661.103